Deliver real business value with AI

AI isn’t a novelty – it’s a competitive advantage. Organizations across industries are learning that AI, when applied strategically, can drive real transformation and efficiency. The best results come when businesses move beyond curiosity to targeted, high-impact solutions.

Why automated decision-making?

One of the most powerful ways to extract value from AI is through automated decision-making, letting AI handle daily high-volume tasks so your teams can focus on strategic work.

Ideal candidates include workflows involving repetitive decision points or unstructured documents: contracts, invoices, compliance reviews, financial reconciliations, fraud checks, operating reports and more. Areas like audits, revenue recognition, compliance validation and fraud detection are especially ripe for AI.

How to get started: A business focused view

1.Identify high-impact areas

Begin with your highest-volume, time-sensitive processes. Look for areas where decisions are frequent, data-rich and outcome-critical. Understanding who leads the process, available data and key performance metrics sets the stage for impact.

2.Assemble the right team 

Success hinges on a balanced, cross-functional team, including:

• An executive sponsor to align the initiative with strategic goals and ensure resources are committed
• Process experts who know the big picture and granular details of key decisions
• Technical leads with AI, data and integration experience, plus a strong understanding of how these decisions are deployed and managed

This combination fosters clarity, accountability and alignment.

3.Simplify decision mapping

Map your current or target process flows in collaboration with frontline users. Break decisions down to their simplest units, this ensures you can measure performance, understand bias risks and maintain transparency. Capture details like:

• How often each decision occurs
• What data inputs are used
• What a good vs. bad decision looks like
• What the business value of correct decisions is
• How quickly you can gauge impact (time-to-value)

These insights form a clear decision inventory and process flow diagram, which guides where automation and AI will drive real benefit.

4.Ensure data readiness

AI is only as good as the data it relies on. Assess your existing data stories for gaps. Key areas to address:

• Data quality, consistency and availability
• Centralizing access in a secure and organized data platform
• Processes to monitor data drift or bias as things change. Machine learning tools can also help fill in missing or inconsistent data through standardization and intelligent inference, making your data AI-ready.

5.Build organizational readiness

Technology adoption isn’t enough on its own. Embed change management into the initiative from day one:

• Prepare the people: Identify skill gaps and train teams on new tools and workflows
• Communicate early and often to build awareness and buy‑in
• Address resistance by clarifying roles, responsibilities and benefits

Transition success depends on enabling ownership and accountability, both at launch and in ongoing operations, to sustain value over the long term.

Four pillars of value-driven AI

When these pillars are in place, your AI efforts are more than experiments, they’re drivers of measurable business impact.

1. Leadership and people: Is there executive backing and cross-functional alignment?
2. Decision clarity: Are key decisions mapped and measurable?
3. Data readiness: Is the data accurate, accessible and governance in place?
4. Organizational readiness: Is the team trained, aligned and prepared to change?

Source: BakerTilly